import json
import pandas as pd
import ipywidgets as widgets
from IPython.display import display, HTML, Image

# Load the JSON data into a Python dictionary
json_file_path = '/home/daniarch/projects/kohya_ss/NB/json/V3_R4_lora_sdxl_prodigy_22_20240119-221944.json'
with open(json_file_path, 'r') as file:
    settings_json = json.load(file)

# Display the JSON file name with larger font size
file_name = json_file_path.split('/')[-1]
display(HTML(f"<h3 style='font-size: 45px;'>{file_name}</h3>"))

# Truncate the value of 'sample_prompts' to 10 characters
if 'sample_prompts' in settings_json:
    settings_json['sample_prompts'] = settings_json['sample_prompts'][:10]

# Convert to a DataFrame
settings_df = pd.DataFrame(list(settings_json.items()), columns=['Setting', 'Value'])

# Convert index to a column and reset the index
settings_df.reset_index(inplace=True)
settings_df.rename(columns={'index': 'No.'}, inplace=True)

# Splitting the DataFrame into eighths
eighth_len = len(settings_df) // 8
eighths = [settings_df.iloc[i*eighth_len:(i+1)*eighth_len] for i in range(8)]

# Handling the case where the total number of rows is not exactly divisible by 8
if len(settings_df) % 8 != 0:
    eighths[-1] = pd.concat([eighths[-1], settings_df.iloc[8*eighth_len:]])

# Adjusting the display options for a compact view
pd.set_option('display.max_columns', 40)
pd.set_option('display.width', 4000)
pd.set_option('display.max_colwidth', None)

# Combining the eight parts side by side
df_display = eighths[0].reset_index(drop=True)
for i in range(1, 8):
    df_display = df_display.join(eighths[i].reset_index(drop=True), rsuffix=f'_{i+1}')

# Apply styling for vertical alignment to the entire DataFrame
styled_df_full_aligned = df_display.style.set_properties(**{'vertical-align': 'top'})

# Display the styled DataFrame
display(styled_df_full_aligned)

# Define the list of important settings
important_keys = [
    'additional_parameters', 'logging_dir', 'min_snr_gamma', 'network_alpha', 
    'network_dim', 'optimizer', 'optimizer_args', 'pretrained_model_name_or_path', 
    'sdxl', 'train_data_dir', 'LoRA_type', 'lr_scheduler', 'learning_rate', 
    'keep_tokens', 'training_comment', 'train_batch_size', 'shuffle_caption', 'max_timestep'
]

# Extract the important settings
important_settings = {k: settings_json.get(k, 'Not available') for k in important_keys}

# Convert to a DataFrame
important_settings_df = pd.DataFrame(list(important_settings.items()), columns=['Setting', 'Value'])

# Display the title for important settings
display(HTML("<h3 style='font-size: 45px;'>Important Settings</h3>"))

# Display the important settings DataFrame
display(important_settings_df)

# Define the image path
image_path = "/home/daniarch/projects/kohya_ss/NB/images_nb/V3_R4_lora_sdxl_prodigy_22.jpg"

# Extracting and displaying the image file name with larger font size
image_file_name = image_path.split('/')[-1]
display(HTML(f"<h3 style='font-size: 45px;'>{image_file_name}</h3>"))

# Display the image
display(Image(image_path))

	No.	Setting	Value	No._2	Setting_2	Value_2	No._3	Setting_3	Value_3	No._4	Setting_4	Value_4	No._5	Setting_5	Value_5	No._6	Setting_6	Value_6	No._7	Setting_7	Value_7	No._8	Setting_8	Value_8
0	0	LoRA_type	Standard	15	color_aug	False	30	full_fp16	False	45	max_resolution	1024,1024	60	network_alpha	32	75	rank_dropout	0	90	save_state	False	105	unet_lr	1.000000
1	1	LyCORIS_preset	full	16	constrain	0.000000	31	gradient_accumulation_steps	1	46	max_timestep	1000	61	network_dim	32	76	rank_dropout_scale	True	91	scale_v_pred_loss_like_noise_pred	False	106	unit	1
2	2	adaptive_noise_scale	0	17	conv_alpha	1	32	gradient_checkpointing	True	47	max_token_length	75	62	network_dropout	0	77	reg_data_dir		92	scale_weight_norms	0	107	up_lr_weight
3	3	additional_parameters	--lr_scheduler_type "CosineAnnealingLR" --lr_scheduler_args "T_max=1000"	18	conv_block_alphas		33	keep_tokens	1	48	max_train_epochs		63	no_token_padding	False	78	rescaled	False	93	sdxl	True	108	use_cp	False
4	4	block_alphas		19	conv_block_dims		34	learning_rate	1.000000	49	max_train_steps	1000	64	noise_offset	0	79	resume		94	sdxl_cache_text_encoder_outputs	False	109	use_scalar	False
5	5	block_dims		20	conv_dim	1	35	logging_dir	/home/daniarch/projects/kohya_ss/V3_R4/log	50	mem_eff_attn	False	65	noise_offset_type	Original	80	sample_every_n_epochs	5	95	sdxl_no_half_vae	True	110	use_tucker	False
6	6	block_lr_zero_threshold		21	debiased_estimation_loss	False	36	lora_network_weights		51	mid_lr_weight		66	num_cpu_threads_per_process	2	81	sample_every_n_steps	0	96	seed	1337	111	use_wandb	True
7	7	bucket_no_upscale	True	22	decompose_both	False	37	lr_scheduler	constant	52	min_bucket_reso	256	67	optimizer	Prodigy	82	sample_prompts	a hand hol	97	shuffle_caption	True	112	v2	False
8	8	bucket_reso_steps	64	23	dim_from_weights	False	38	lr_scheduler_args		53	min_snr_gamma	5	68	optimizer_args	decouple=True weight_decay=0.45 d_coef=2 use_bias_correction=True safeguard_warmup=True betas=0.9,0.99	83	sample_sampler	dpm_2	98	stop_text_encoder_training_pct	0	113	v_parameterization	False
9	9	cache_latents	True	24	down_lr_weight		39	lr_scheduler_num_cycles		54	min_timestep	0	69	output_dir	/home/daniarch/projects/kohya_ss/V3_R4/model	84	save_every_n_epochs	1	99	text_encoder_lr	1.000000	114	v_pred_like_loss	0
10	10	cache_latents_to_disk	True	25	enable_bucket	True	40	lr_scheduler_power		55	mixed_precision	bf16	70	output_name	V3_R2_lora_sdxl_prodigy_22	85	save_every_n_steps	0	100	train_batch_size	4	115	vae
11	11	caption_dropout_every_n_epochs	0.000000	26	epoch	0	41	lr_warmup	0	56	model_list	custom	71	persistent_data_loader_workers	False	86	save_last_n_steps	0	101	train_data_dir	/home/daniarch/projects/kohya_ss/V3_R4/img	116	vae_batch_size	0
12	12	caption_dropout_rate	0	27	factor	-1	42	max_bucket_reso	2048	57	module_dropout	0	72	pretrained_model_name_or_path	/home/daniarch/miniconda3/envs/autoLDM/stable-diffusion-webui/models/Stable-diffusion/sd_xl_base_1.0.safetensors	87	save_last_n_steps_state	0	102	train_norm	True	117	wandb_api_key	647fd09ca3b2d990b3cf95aa7598f849b02fb9c8
13	13	caption_extension	.txt	28	flip_aug	False	43	max_data_loader_n_workers	0	58	multires_noise_discount	0	73	prior_loss_weight	1.000000	88	save_model_as	safetensors	103	train_on_input	True	118	weighted_captions	False
14	14	clip_skip	1	29	full_bf16	False	44	max_grad_norm	1	59	multires_noise_iterations	0	74	random_crop	False	89	save_precision	bf16	104	training_comment	4 repeat	119	xformers	xformers